Jason Heppler hepplerj

## twitterusers.py
#!/usr/bin/python3

import tweepy
import csv

consumer_key = ""
consumer_secret = ""

access_token =  ""
access_token_secret = ""

## metro.R
library(tidycensus)
library(tidyverse)

# If not set, un-comment below and install your Census API key (https://api.census.gov/data/key_signup.html)
# census_api_key("YOUR KEY HERE", install = TRUE)

get_acs(geography = "metropolitan statistical area/micropolitan statistical area",
        variables = "DP03_0021PE",
        summary_var = "B01003_001",
        survey = "acs1",

## census_cleanup.R
library(tidyverse)
library(tidycensus)

# My recommendation is to use the tidycensus library to make getting this data
# easier than reading in the data from the Census website.
#
# Before you can begin, you'll need to get an API key from the Census Bureau.
# You can acquire one here:
#
# Once you have the API key, run the following in RStudio:

## messy.R
library(charlatan)
library(salty)
library(magrittr)
library(readr)

messydata <- ch_generate('name','job','phone_number', n = 200)

messydata <- messydata %>%
  mutate(job = salt_capitalization(job)) %>%
  mutate(phone_number = salt_na(phone_number)) %>%

## frequency_to_list.R
library(tidyverse)
library(readxl)

data <- readxl::read_xlsx("data.xlsx")
reshaped <- data %>% gather(word, freq, 2:21)
reshaped <- reshaped %>% drop_na()

cleaned <- reshaped %>%
  uncount(freq)

## geofilter.R
library(tidyverse)
library(maps)
library(mapdata)

data <- read_csv("~/Desktop/nplsuperfund.csv")
names(data) <- c("lat","lon","date")

# Filter down to USA extent to remove extraneous points
tidy <- data %>%
  filter(lat < -67, lat > -125) %>%

## hex_logo.R
library(hexSticker)
library(tidyverse)
library(tidycensus)
library(sf)
library(viridis)

options(tigris_use_cache = TRUE)

nebraska_raw <- get_acs(state = "NE",
                        geography = "tract",

## pandas.py
# Unique values in a dataframe column
df['column_name'].unique()

# Grab dataframe rows where column = value
df = df.loc[df.column == 'some_value']

# Grab dataframe rows where column value is present in a list
value_list = ['value1', 'value2', 'value3']
df = df.loc[:,df.columns.isin(valuelist)]
# or grab rows where a value is not present in a list

## README.md

      
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                hepplerj
                / README.md
            
            
              Last active May 10, 2018
            
              
                Add leaflet points on click
              
          
      View README.md
  
      
    Click on the map to add points. See the console for lat/long output.

  
## index.html
<!DOCTYPE html>
<head>
  <meta charset="utf-8">
  <script src="https://d3js.org/d3.v4.min.js"></script>
  <script src="http://www.webglearth.com/v2/api.js"></script>

  <script>
  function map() {
    var options = { zoom: 1.5, position: [47.19537,8.524404] };
    var earth = new WE.map('earth_div', options);
	#!/usr/bin/python3

	import tweepy
	import csv

	consumer_key = ""
	consumer_secret = ""

	access_token = ""
	access_token_secret = ""
	library(tidycensus)
	library(tidyverse)

	# If not set, un-comment below and install your Census API key (https://api.census.gov/data/key_signup.html)
	# census_api_key("YOUR KEY HERE", install = TRUE)

	get_acs(geography = "metropolitan statistical area/micropolitan statistical area",
	variables = "DP03_0021PE",
	summary_var = "B01003_001",
	survey = "acs1",
	library(tidyverse)
	library(tidycensus)

	# My recommendation is to use the tidycensus library to make getting this data
	# easier than reading in the data from the Census website.
	#
	# Before you can begin, you'll need to get an API key from the Census Bureau.
	# You can acquire one here:
	#
	# Once you have the API key, run the following in RStudio:
	library(charlatan)
	library(salty)
	library(magrittr)
	library(readr)

	messydata <- ch_generate('name','job','phone_number', n = 200)

	messydata <- messydata %>%
	mutate(job = salt_capitalization(job)) %>%
	mutate(phone_number = salt_na(phone_number)) %>%
	library(tidyverse)
	library(readxl)

	data <- readxl::read_xlsx("data.xlsx")
	reshaped <- data %>% gather(word, freq, 2:21)
	reshaped <- reshaped %>% drop_na()

	cleaned <- reshaped %>%
	uncount(freq)
	library(tidyverse)
	library(maps)
	library(mapdata)

	data <- read_csv("~/Desktop/nplsuperfund.csv")
	names(data) <- c("lat","lon","date")

	# Filter down to USA extent to remove extraneous points
	tidy <- data %>%
	filter(lat < -67, lat > -125) %>%
	library(hexSticker)
	library(tidyverse)
	library(tidycensus)
	library(sf)
	library(viridis)

	options(tigris_use_cache = TRUE)

	nebraska_raw <- get_acs(state = "NE",
	geography = "tract",
	# Unique values in a dataframe column
	df['column_name'].unique()

	# Grab dataframe rows where column = value
	df = df.loc[df.column == 'some_value']

	# Grab dataframe rows where column value is present in a list
	value_list = ['value1', 'value2', 'value3']
	df = df.loc[:,df.columns.isin(valuelist)]
	# or grab rows where a value is not present in a list
	<!DOCTYPE html>
	<head>
	<meta charset="utf-8">
	<script src="https://d3js.org/d3.v4.min.js"></script>
	<script src="http://www.webglearth.com/v2/api.js"></script>

	<script>
	function map() {
	var options = { zoom: 1.5, position: [47.19537,8.524404] };
	var earth = new WE.map('earth_div', options);